Open source person re-identification library in python

https://www.kaggle.com/c/humpback-whale-identification

Code for the NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

The easiest way to use metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

This is the implementation of paper <Additive Margin Softmax for Face Verification>

Official source code of "Batch DropBlock Network for Person Re-identification and Beyond" (ICCV 2019)

Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor learning loss"

PyTorch Implementation for Deep Metric Learning Pipelines

PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016.

Source code for the paper "Divide and Conquer the Embedding Space for Metric Learning", CVPR 2019

Official pytorch Implementation of Relational Knowledge Distillation, CVPR 2019

Deep metric learning methods implemented in Chainer

A PyTorch framework for an image retrieval task including implementation of N-pair Loss (NIPS 2016) and Angular Loss (ICCV 2017).

Deep Face Recognition in PyTorch

Angular penalty loss functions in Pytorch (ArcFace, SphereFace, Additive Margin, CosFace)

SegSort: Segmentation by Discriminative Sorting of Segments

Code for Supervised Word Mover's Distance (SWMD)

code for ICCV19 paper "Deep Meta Metric Learning"

Local Fisher Discriminant Analysis in R

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease (AMIA 2018)

R package for Distance Metric Learning

Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval (CVPR 2019)

Code for CVPR 2019 paper "Deep Metric Learning to Rank"

Fully convolutional metric learning for geometric image correspondences.

PhotoSynth Dataset for improving local patch Descriptors

A highly-configurable tool that enables thorough evaluation of metric-learning algorithms.

Project page for Heated-up Softmax Embedding

Papers and Codes about Deep Metric Learning/Deep Embedding